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Chapter 2 - Traditional AI

Machine Learning is not new. The field of Artificial Intelligence (AI) has been around since the 1950s, and many of the techniques we use today have their roots in traditional AI methods.

Traditional AI can help you with:

  • Predictive Analytics
  • Classification: Spam detection, sentiment analysis, Fraud detection
  • Clustering: Customer segmentation, Anomaly detection
  • Optimization: Resource allocation, Scheduling

With traditional AI, you need to train a model, usually by splitting your data into 3 buckets:

  • 60% Training Data
  • 20% Validation Data
  • 20% Test Data

The result of the training will be a machine learning model. If the training is done right, the model will perform well. However, if the problem is too simple, you will have an Underfitting problem. If the problem is too complex, you will have an Overfitting problem.

Each one can be fixed by the following:

Underfitting:

  • Add more features (more data)
  • Increase the model complexity (linear->polynomial)
  • Change model Algorithm / Architecture (more layers/neurons)
  • Reduce Regularization (penalty on the loss_function)
  • More training epochs (batches)

Overfitting:

  • The model is too complex and captures the noise in the data
  • More training data
  • Reduce the model complexity
  • Reduce the number of features
  • Increase Regularization
  • Early Stopping

Learning Options

For traditional AI, you have basically a couple of different approaches like:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Semi-Supervised Learning

Supervised Learning

This means that you have labeled data. For example, you have a dataset of images of cats and dogs, and each image is labeled as either “cat” or “dog.” The model learns to classify new images based on the labeled examples.

Use cases are:

  • Spam Detection: Spam vs Ham
  • Image Recognition: Hotdog vs Not Hotdog
  • Regression: Predict House Pricing, Predict Stock Prices.
  • Customer Churn Prediction: Churn vs Not Churn
  • Credit Scoring Prediction: Good vs Bad

Main algorithms for regression and classification are:

Regression:

  • Linear Regression
  • Decision Trees
  • Random Forest
  • Gradient Boosting
  • Support Vector Machines

Classification:

  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Gradient Boosting
  • Support Vector Machines
  • Naive Bayes